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A two-stage approach solo_GAN for overlapping cervical cell segmentation based on single-cell identification and boundary generation

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Abstract

Accurate cell segmentation is a pivotal step throughout the cervical cancer treatment continuum, encompassing early screening, guiding treatment decisions, and assessing long-term prognosis. Currently, in clinical practice, pathologists rely on microscopic examination of cell characteristics followed by manual annotation, leveraging their expert knowledge. Nonetheless, this approach is labor-intensive, time-consuming, and subject to subjectivity. While existing segmentation methods successfully delineate cell clusters, they struggle with single-cell segmentation in overlapping scenarios, often relying on pixel classification methods. This approach tends to produce discontinuous boundaries and incomplete contours. In this paper, we introduce a two-phase framework, Solo_GAN, capable of generating complete boundaries for single cells in overlapping cell clusters and complex backgrounds. In the first phase of our framework, we propose a target detection model based on YOLOv3 for identifying single-cell regions of interest (ROI) in Pap cervical images. In the second stage, the ROIs are fed into a novel network called the dual-domain mapping segmentation network, which is used to generate complete single-cell boundary maps. The process involves conversion between cervical cell images in different states, achieving the generation of single-cell boundaries while retaining the original features of the image. Our method has been extensively evaluated on a Hybrid cervical cell dataset (public and private sets) for its effectiveness. The results demonstrate that our approach consistently outperforms the state-of-the-art methods and proves highly effective. We provide visualizations of the output at each processing stage and compare them with mainstream methodologies. The cell segmentation method proposed in this paper holds considerable significance for clinical research on cells in different stages of cervical cancer.

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Data availability

The data used in this study are available upon request from the corresponding author. Due to privacy and ethical considerations, some data may be subject to restrictions. Requests for data access should be directed to [22115032@bjtu.edu.cn].

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China

    Zihao He, Dongyao Jia, Chuanwang Zhang, Ziqi Li & Nengkai Wu

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Contributions

Dongyao Jia and Zihao He conceived and designed the study. Chuanwang Zhang and Ziqi Li. conducted data collection and analysis. Zihao He drafted the manuscript. Dongyao Jia critically reviewed and revised the manuscript. All authors approved the final version for publication.

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Correspondence to Dongyao Jia.

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This study was conducted in accordance with the ethical guidelines and regulations set forth by Guangdong Provincial People’s Hospital Ethics Committee. Prior to data collection, written informed consent was obtained from all participants involved in the study.

The data used in this study were de-identified and stored securely to maintain confidentiality. Only authorized researchers had access to the data, and all data handling and analysis procedures complied with applicable privacy laws and regulations.

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He, Z., Jia, D., Zhang, C. et al. A two-stage approach solo_GAN for overlapping cervical cell segmentation based on single-cell identification and boundary generation. Appl Intell 54, 4621–4645 (2024). https://doi.org/10.1007/s10489-024-05378-1

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